POST
/
ai
/
openai
/
chat
/
completions
Generate chat completions in OpenAI format
curl --request POST \
  --url https://api.messari.io/ai/openai/chat/completions \
  --header 'Content-Type: */*' \
  --header 'X-Messari-API-Key: <api-key>' \
  --data '{
  "allow_clarification_query": true,
  "generate_related_questions": 123,
  "inline_citations": true,
  "messages": [
    {
      "MultiContent": [
        {
          "image_url": {
            "detail": "<string>",
            "url": "<string>"
          },
          "text": "<string>",
          "type": "<string>"
        }
      ],
      "content": "<string>",
      "function_call": {
        "arguments": "<string>",
        "name": "<string>"
      },
      "name": "<string>",
      "reasoning_content": "<string>",
      "refusal": "<string>",
      "role": "<string>",
      "tool_call_id": "<string>",
      "tool_calls": [
        {
          "function": {
            "arguments": "<string>",
            "name": "<string>"
          },
          "id": "<string>",
          "index": 123,
          "type": "<string>"
        }
      ]
    }
  ],
  "response_format": "<string>",
  "stream": true,
  "verbosity": "<string>"
}'
{
  "choices": [
    {
      "finish_reason": "<string>",
      "index": 123,
      "message": {
        "content": "<string>",
        "role": "<string>"
      }
    }
  ],
  "created": 123,
  "id": "<string>",
  "metadata": {
    "charts": [
      {
        "citationId": 123,
        "dataset": "<string>",
        "end": "2023-11-07T05:31:56Z",
        "entities": [
          {
            "entityId": "<string>",
            "entityType": "<string>"
          }
        ],
        "granularity": "<string>",
        "id": 123,
        "metric": "<string>",
        "metricTimeseries": {
          "point_schema": [
            {
              "attribution": [
                {
                  "link": "<string>",
                  "name": "<string>"
                }
              ],
              "description": "<string>",
              "format": "<string>",
              "group_aggregate_operation": "<string>",
              "id": "<string>",
              "is_timestamp": true,
              "name": "<string>",
              "slug": "<string>",
              "subcategory": "<string>",
              "time_bucket_aggregate_operation": "<string>"
            }
          ],
          "series": [
            {
              "entity": {},
              "key": "<string>",
              "points": [
                [
                  "<any>"
                ]
              ]
            }
          ]
        },
        "start": "2023-11-07T05:31:56Z",
        "tier": "<string>"
      }
    ],
    "related_questions": [
      "<string>"
    ],
    "sources": [
      {
        "citationId": 123,
        "domain": "<string>",
        "title": "<string>",
        "url": "<string>"
      }
    ],
    "status": "<string>",
    "trace_id": "aSDinaTvuI8gbWludGxpZnk="
  },
  "model": "<string>",
  "object": "<string>"
}
It follows OpenAI’s chat completions API specification for easy integration with libraries like OpenAI’s official python SDK. If you’d like to play around with the responses feel free to try on the Messari web-app, it uses the same underlying endpoint implementation. The chat completion endpoint leverages a graph architecture with agents to:
  • Access Messari’s real-time quantitative (compute) dataset which includes but is not limited to: Market data, Asset metrics, Fundraising, Token unlocks
  • Access Messari’s qualitative (search) dataset which includes but is not limited to: News, Blogs, Youtube transcriptions, RSS-feeds, Twitter, Webcrawl documents, Proprietary datasets of: Research, Quarterlies, Diligence Reports
  • Generate market insights and analysis
  • Process natural language queries about crypto assets, protocols and projects
For a more interactive experience trying out the API, here is our Replit in Typescript. Simply populate the API_KEY field with your Enterprise API key generated in the Messari Account > API page of our webapp and you’re off!

​
Request Params

​
verbosity

The verbosity parameter controls the level of detail in the model’s response. When set to "verbose", the model provides more comprehensive and detailed explanations, including additional context, examples, and supporting information. Other possible values might include "balanced" or "succinct" for shorter responses. Example usage: 
{
  "verbosity": "verbose"
}

​
response_format

The response_format parameter specifies the desired formatting style for the model’s response. When set to "markdown", the output will be formatted using Markdown syntax, allowing for structured text with headings, lists, code blocks, and other formatting elements. Other common option might include "text". Example usage: 
{
  "response_format": "markdown"
}

​
inline_citations

The inline_citations parameter determines whether the response metadata should include citations within its response text. When set to true, the model will reference sources directly in the text where information is being drawn from. This is particularly useful for academic, research, or factual content where attribution is important. Example usage: 
{
  "inline_citations": true
}

​
stream

The stream parameter controls whether the API response is delivered as a complete response or as a stream of partial responses. When set to false, the API will wait until the entire response is generated and then deliver it in one piece. When set to true, the API would begin sending partial responses as they are generated, useful for implementing real-time typing effects or processing responses incrementally. Example usage: 
{
  "stream": false
}
The generate_related_questions parameter determines whether the response metadata should include AI-generated related questions to the user query. This feature is useful for receiving follow up questions as part of the API response payload to prompt the user with potential next questions. Note: A maximum of 5 is allowed. Example usage: 
{
  "generate_related_questions": 2
}

Authorizations

​
X-Messari-API-Key
string
header
required

Body

*/*
​
messages
object[]
required

Array of messages in the conversation history

​
allow_clarification_query
boolean

Whether the AI can ask for clarification when the query is ambiguous

​
generate_related_questions
integer

Adds AI generated related questions to use as follow up questions

​
inline_citations
boolean

Adds inline citation document references in the payload metadata

​
response_format
string

Text format for the response. Values accepted are 'markdown' and 'plaintext'.

​
stream
boolean

Stream the response through the API

​
verbosity
string

Controls the length and detail level of the AI-generated response. Values accepted are 'succinct', 'balanced', and 'verbose'.

Response

Default response

​
choices
object[]
required

Array of completion choices

​
created
integer
required

Unix timestamp when the completion was created

​
id
string
required

Unique identifier for the completion request

​
model
string
required

Model used for the completion

​
object
string
required

Type of object returned, always 'chat.completion'

​
metadata
object

Additional metadata about the completion